Gait refers to a person's walking style (that is, the “way” the person walks). There is strong evidence from psychophysical experiments and gait analysis research (a multi-disciplinary field that spans kinesiology, physiotherapy, orthopedic surgery, ergonomics, etc.) that gait dynamics contain a signature that is characteristic of, and possibly unique to, each individual. More specifically, from a biomechanics standpoint, human gait consists of synchronized, integrated movements of hundreds of muscles and joints of the body. These movements follow the same basic bipedal pattern for all humans, and yet vary from one individual to another in certain details (such as their relative timing and magnitudes) as a function of their entire musculo-skeletal structure, e.g., body mass, limb lengths, bone structure, etc. Because this structure is difficult to replicate, gait is believed to be unique to each individual and can be characterized by a few hundred kinematic parameters, namely the angular velocities and accelerations at certain joints and body landmarks.
Various techniques have been proposed for automatically analyzing a person's gait for use in different applications. One such application is gait recognition. In the computer vision community, gait recognition refers to the task of automatically extracting visual cues that characterize the motion of a walking person from video images of the walking person, and using these cues to potentially identify the person. Gait is an attractive biometric for automated recognition, particularly for passive surveillance applications, due to the ability to determine this biometric “at a distance,” that is, without the need to interact with the subject, or even obtain the cooperation of the subject. Gait is also a biometric that may be difficult to conceal.
Existing automated approaches to analyzing human gait can be categorized as either model-based or holistic. Model-based approaches use a model of either the person's shape (e.g., structure) or motion in order to recover features of gait mechanics, such as stride dimensions and kinematics of joint angles. In holistic techniques, gait is characterized by the statistics of the spatiotemporal patterns generated by the silhouette of the walking person in the image. That is, holistic techniques compute a set of features (the gait signature) from these patterns, and then use these features to classify gait. Some studies have required the subject to wear special instruments (such as LED markers), or walk on special surfaces. Other studies have attempted to characterize a person's gait without any such artificial cues and assumptions.
While the above-identified techniques have shown promise in identifying individuals based on their gait, there remains room for significant improvement in this field. More specifically, because of the complexity of human locomotion, it is not an intuitive matter what collection of parameters associated with a subject's ambulatory motion can best be used to characterize the subject's unique gait. It is likewise a non-routine task to provide a reliable technique for extracting identified parameters from video data. Various real-world conditions may further compound these challenges, such as the possibility that various factors may impact the subject's gait behavior in ways that may be difficult to predict (including factors of fatigue of the subject, mood of the subject, sickness of the subject, footwear used by the subject, and so on), the possibility that environmental conditions may degrade the quality of the captured images, the possibility that the subject's apparel may obscure the gait behavior, the possibility that the video itself may be of relatively low resolution, and so on. These factors may result in gait analysis results that have a considerable degree of error.
As such, there is an exemplary need in the art to provide motion-based biometric analysis having improved accuracy, reliability, utility, and/or efficiency compared to known techniques.